kml3d (version 0.6)

choice: ~ Function: choice ~

Description

choice lets the user choose some Clustering he wants to export.

Usage

choice(object, typeGraph = "bmp",...)

Arguments

object

[ClusterLongData]: Object containing the trajectories and all the clusterizations found by kml, from whom the user want to export some Clustering.

typeGraph

[character] for every selected clustering, choice export some graphs. typeGraph set the format that will be used. Possible formats are bmp,

...

Parameters for the function bmp, jpeg, png or tiff.

Value

• No value are return; Several files and graphes are created in the curent folder, see 'details'.

code

plot,ListClustering

itemize

• objectName-criterionActif.ext

item

• name-Detail.csv
• name-TrajMean.csv
• name-Traj.ext
• objectName-criterionAll.ext

Author(s)

Christophe Genolini INSERM U669 / PSIGIAM: Paris Sud Innovation Group in Adolescent Mental Health Modal'X / Universite Paris Ouest-Nanterre- La Defense Contact author : [e-mail]

Details

choice is a function that lets the user see the Clustering found by kml. At first, choice opens a graphics window. On the left side are qualities criterion of all the Clustering contained in Object. One Clustering is 'active', it is the one marked by a black dot. On the right side, the trajectories of object are drawn, according to the active Clustering. From there, choice offers numerous options.

• Arrow

{Change the active Clustering.} Space{Select/unselect a Clustering (the selected Clustering are surrounded by a circle).} Return{Export the selected Clustering, then quit the function choice.} 'e'{Switch between the different display of the trajectories and the criterion.} 'd'{Change the quality criterion.} 'c'{Order the Clustering according the the actual quality criterion.} 'r'{Change the trajectories style.} 't'{Change the mean trajectories style.} 'g/t'{Decrease / increase the size of the mean trajectories symbols.} 'h/y'{Decrease / increase the number of symbol on the means trajectories.}

References

Article "KmL: K-means for Longitudinal Data", in Computational Statistics, Volume 25, Issue 2 (2010), Page 317. Web site: http://christophe.genolini.free.fr/kml

Examples

### Creation of articficial data
cld1 <- gald(c(15,15,15))

### Clusterisation
kml(cld1,nbRedrawing=3)

### Selection of the clusterization we want
#     (linux does not support getGraphicsEvent...
try(choice(cld1))